Extended balancing ontological and operational factors in refining multiagent neighborhood using ACO

In this paper, we present our work extended balancing ontological and operational factors using Ant Colony Optimization (ACO) in building collaborations within multiagent neighborhoods. This innovation overcomes the previous version's problem on better collaboration among agents, reducing message flooding, increase scalability and resolved the “hidden boundary” problem. The domain of application is multiagent, distributed information retrieval, where agents safeguarding their own information or data resources, improve their local services by collaborating with others. Information retrieval is like a food finding for ants. Foods are not always available at the same location and there might be a better location with more foods. Same thing apply to information. In this paper, we adopted ACO as our agent's collaboration technique to enhance the previous work.